Improved SVM regression using mixtures of kernels

Kernels are used in support vector machines to map the learning data (nonlinearly) into a higher dimensional feature space where the computational power of the linear learning machine is increased. Every kernel has its advantages and disadvantages. A desirable characteristic for learning may not be a desirable characteristic for generalization. Preferably the 'good' characteristics of two or more kernels should be combined. It is shown that using mixtures of kernels can result in having both good interpolation and extrapolation abilities. The performance of this method is illustrated with an artificial as well as an industrial data set.